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Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system.

A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors